1. Technical Field
This description pertains generally to deformable image registration, and more particularly to deformable image registration for head and neck anatomy.
2. Background Discussion
The term head and neck cancer (HNC) refers to a group of biologically similar cancers originating from the upper aero digestive tract, including the lip, oral cavity (mouth), nasal cavity, Para nasal sinuses, pharynx, and larynx. 90% of head and neck cancers are squamous cell carcinomas (SCCHN), originating from the mucosal lining (epithelium) of these regions. HNC often spread to the lymph nodes of the neck, leading to cancer metastasis in the rest of the patient's body. Radiotherapy (RT) has seen a major push towards treatment plans for the HNC that are tailored to the patient and adapted to their radiation response. Ignoring patient misalignments caused by non-rigid changes in patient posture and physiology can lead to under-dosing the tumor and over-irradiating the healthy tissue. Image-guided analyses of such non-rigid head and neck anatomy variations were made possible by use of deformable image registration (DIR) frameworks that register the patient planning anatomy with the treatment anatomy. Such analyses have led to several indications on the need for better patient aligning. For instance, it has been shown that uncorrected patient positioning misalignments would increase the maximum dose to both the brainstem and spinal cord by 10 Gy and the mean dose to the left and right parotid glands by 7.8 and 8.5 Gy, respectively. Similarly, 95% of the gross tumor volume (GTV) and clinical target volume (CTV) would decrease by 4 Gy and 5.6 Gy, respectively.
The accuracy of DIR to help quantify patient posture and physiological changes is instrumental for the success of adaptive RT. Adaptive RT employs quantitative dose delivery error characterization and subsequent compensatory strategies. However, DIR development has been hampered by a lack of techniques that generate ground-truth deformations that can be used to evaluate competing DIR algorithms. Biomechanical human anatomy models have been developed for applications ranging from computer animation to CT image registration.
Sophisticated biomechanical models have been developed for individual anatomical sites, including the head and neck, the hand, lungs, and the leg. Such models, when developed from patient CT or MRI, can create subject-specific physiological and musculoskeletal dynamic atlas. As an example, subject specific cardiac models of normal and diseased heart have been developed using Non-Uniform Rational Bezier Splines (NURBS) in order to simulate the cardiac motion before and after the treatment. Physics-based methods, such as Finite Element Methods and Mass-Spring Models, have been applied for deforming anatomy of the torso, and the biomechanical nature of these models also allows for the inclusion of subject specific tumor representations and day-to-day variations in the treatment.